Week 5A
Getting Data Part 1: Working with APIs

Oct 4, 2021

Week #5 Agenda¶

Introduction to APIs
Pulling census data and shape files using Python
- Example: Lead poisoning in Philadelphia
Using the Twitter API
- Plotting geocoded tweets
- Word frequencies
- Sentiment analysis

import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import Point

from matplotlib import pyplot as plt
import seaborn as sns

import hvplot.pandas
import holoviews as hv

# UNCOMMENT TO SEE ALL ROWS/COLUMNS IN DATAFRAMES
pd.options.display.max_columns = 999
pd.options.display.max_rows = 999

Introduction to APIs¶

Or, how to pull data from the web using Python

Example APIs¶

Socrata Open Data: https://dev.socrata.com/
Open Data Philly: https://opendataphilly.org
US Census Bureau: https://www.census.gov/data/developers/data-sets.html

Bureau of Labor Statistics: https://www.bls.gov/developers/
US Geological Survey: https://www.usgs.gov/products/data-and-tools/apis
US Environmental Protection Agency: https://www.epa.gov/enviro/web-services

Google APIs: https://console.developers.google.com/apis/library?project=cp255-185021
Facebook: https://developers.facebook.com/docs/apis-and-sdks/
Twitter: https://developer.twitter.com/en/docs/api-reference-index.html

Foursquare: https://developer.foursquare.com/
Instagram: https://www.instagram.com/developer/
Yelp: https://www.yelp.com/developers

Note: when accessing data via API, many services will require you to register an API key to prevent you from overloading the service with requests

Part 1: Reading an automated data feed¶

USGS real-time earthquake feeds¶

This is an API for near-real-time data about earthquakes, and data is provided in GeoJSON format over the web.

The API has a separate endpoint for each version of the data that users might want. No authentication is required.

API documentation:

http://earthquake.usgs.gov/earthquakes/feed/v1.0/geojson.php

Sample API endpoint, for magnitude 4.5+ earthquakes in past day:

http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/4.5_day.geojson

GeoPandas can read GeoJSON from the web directly¶

Simply pass the URL to the gpd.read_file() function:

# Download data on magnitude 2.5+ quakes from the past week
endpoint_url = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_week.geojson"
df = gpd.read_file(endpoint_url)

df.head()

Let's plot them on a map:¶

fig, ax = plt.subplots(figsize=(10, 10))

# plot the country outline
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
world.to_crs(epsg=3857).plot(ax=ax, facecolor="none", edgecolor="black")

# plot the earthquakes
df.to_crs(epsg=3857).plot(ax=ax, color="crimson")

ax.set_axis_off()

Part 2: GeoServices¶

A GeoService is a standardized format for returning GeoJSON files over the web.

Documentation http://geoservices.github.io/

OpenDataPhilly provides GeoService API endpoints for the geometry hosted on its platform

Example: Philadelphia ZIP codes¶

https://www.opendataphilly.org/dataset/zip-codes

# base URL
url = "https://services.arcgis.com/fLeGjb7u4uXqeF9q/arcgis/rest/services/Zipcodes_Poly/FeatureServer/0/"

Small utility package to make querying GeoServices easier: `esri2gpd`¶

https://github.com/PhiladelphiaController/esri2gpd

import esri2gpd

zip_codes = esri2gpd.get(url)

zip_codes.head()

zip_codes.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Let's plot it¶

fig, ax = plt.subplots(figsize=(6, 6))
zip_codes.to_crs(epsg=3857).plot(ax=ax, facecolor="none", edgecolor="black")
ax.set_axis_off()

Another useful example: census tracts¶

https://www.opendataphilly.org/dataset/census-tracts

Part 3: Downloading Philly data from CARTO¶

Philadelphia hosts the majority of its open data in the cloud using CARTO.
They provide an API to download the data
You can access the API documentation from OpenDataPhilly

Note: the "API documentation" on OpenDataPhilly will link to the documentation for the CARTO database

For example: shooting victims in Philadelphia

https://www.opendataphilly.org/dataset/shooting-victims

Download link is just querying an API!¶

https://phl.carto.com/api/v2/sql?q=SELECT+*,+ST_Y(the_geom)+AS+lat,+ST_X(the_geom)+AS+lng+FROM+shootings&filename=shootings&format=csv&skipfields=cartodb_id

First line: the base URL for CARTO
Second line: the SQL query

SQL Databases¶

The CARTO databases can be queried using SQL. This allows you to select specific data from the larger database.

CARTO API documentation: https://carto.com/developers/sql-api/

SQL documentation: https://www.postgresql.org/docs/9.1/sql.html

General Query Syntax¶

SELECT [field names] FROM [table name] WHERE [query]

Let's try it out in Python¶

We'll use Python's requests library to query the API endpoint with our desired query.

import requests

# the API end point
API_endpoint = "https://phl.carto.com/api/v2/sql"

# the query
query = "SELECT * FROM shootings" # table name is "shootings"

# desired format of the returned features
output_format = 'geojson'

# fields to skip
skipfields = ["cartodb_id"]

# all of our request parameters
params = dict(q=query, format=output_format, skipfields=skipfields)

params

{'q': 'SELECT * FROM shootings',
 'format': 'geojson',
 'skipfields': ['cartodb_id']}

# make the request
r = requests.get(API_endpoint, params=params)

r

<Response [200]>

# Get the returned data in JSON format
# This is a dictionary
features = r.json()

# What are the keys?
list(features.keys())

['type', 'features']

features['type']

'FeatureCollection'

# Let's look at the first feature
features['features'][0]

{'type': 'Feature',
 'geometry': {'type': 'Point', 'coordinates': [-75.16119, 40.045822]},
 'properties': {'objectid': 1299122,
  'year': 2021,
  'dc_key': '202114009208.0',
  'code': '411',
  'date_': '2021-02-20T00:00:00Z',
  'time': '19:40:00',
  'race': 'B',
  'sex': 'M',
  'age': '44',
  'wound': 'Multiple',
  'officer_involved': 'N',
  'offender_injured': 'N',
  'offender_deceased': 'N',
  'location': '5600 BLOCK BOYER ST',
  'latino': 0,
  'point_x': -75.16119015,
  'point_y': 40.04582159,
  'dist': '14',
  'inside': 0,
  'outside': 1,
  'fatal': 0}}

Use the GeoDataFrame.from_features() function to create a GeoDataFrame

shootings = gpd.GeoDataFrame.from_features(features, crs="EPSG:4326")

shootings.head()

At-Home Exercise: Visualizing shootings data¶

Step 1: Prep the data¶

Drop rows where the geometry is NaN
Convert to a better CRS (e.g., 3857)
Trim to those only within city limits (some shootings have wrong coordinates, outside Philadelphia!)

city_limits = gpd.read_file(
    "https://opendata.arcgis.com/datasets/405ec3da942d4e20869d4e1449a2be48_0.geojson"
).to_crs(epsg=3857)

Step 2: Plot the points¶

A quick plot with geopandas to show the shootings as points

Step 3: Make a (more useful) hex bin map¶

Count the total number of rows in a table¶

The COUNT function can be applied to count all rows.

query = "SELECT COUNT(*) FROM shootings"

params = dict(q=query)
r = requests.get(API_endpoint, params=params)

r.json()

{'rows': [{'count': 10643}],
 'time': 0.003,
 'fields': {'count': {'type': 'number', 'pgtype': 'int8'}},
 'total_rows': 1}

Important: always good to check how many rows you might be downloading before hand.

Select all columns, limiting the total number returned¶

The LIMIT function limits the number of returned rows. It is very useful for taking a quick look at the format of a database.

# select the first 5
query = "SELECT * FROM shootings LIMIT 5"

params = dict(q=query, format="geojson")
r = requests.get(API_endpoint, params=params)

df = gpd.GeoDataFrame.from_features(r.json(), crs="EPSG:4326")
df

Select by specific column values¶

shootings.head()

Select fatal shootings only¶

query = "SELECT * FROM shootings WHERE fatal = 1"

# Make the request
params = dict(q=query, format="geojson")
r = requests.get(API_endpoint, params=params)

# Make the GeoDataFrame
fatal = gpd.GeoDataFrame.from_features(r.json(), crs="EPSG:4326")
print("number of fatal shootings = ", len(fatal))

number of fatal shootings =  2080

Select shootings in 2021¶

query = "SELECT * FROM shootings WHERE date_ > '1/1/21'"

# Make the request
params = dict(q=query, format="geojson")
r = requests.get(API_endpoint, params=params)

# Make the GeoDataFrame
this_year = gpd.GeoDataFrame.from_features(r.json(), crs="EPSG:4326")
print("number of shootings this year = ", len(this_year))

number of shootings this year =  1687

The easier way: `carto2gpd`¶

A small utility library to simplify the querying of CARTO APIs.
The get() function will query the database
The get_size() function will use COUNT() to get the total number of rows

https://github.com/PhiladelphiaController/carto2gpd

import carto2gpd

where = "date_ > '1/1/21' and fatal = 1"
df = carto2gpd.get(API_endpoint, 'shootings', where=where)

df.head()

# Limit results to the first 5
df = carto2gpd.get(API_endpoint, 'shootings', limit=5)

df

size = carto2gpd.get_size(API_endpoint, 'shootings')
print(size)

10643

At-Home Exercise: Explore trends by month and day of week¶

Step 1:¶

Convert the date column to DateTime objects
Add Month and Day of Week columns

Step 2: Calculate number of shootings by month and day of week¶

Use the familiar Groupby --> size()

Step 3: Make a heatmap using `hvplot`¶

Trends: more shootings on the weekends and in the summer months¶

API Example #1: Census¶

US Census data is foundational¶

Rich data sets with annual releases
Decennial results plus American Community Survey (ACS) results
Wide range of topics covered: sex, income, poverty, education, housing

Getting census data (the old way)
American Factfinder

Getting census data (the new way)
census.data.gov

Getting census data: other options¶

Several 3rd party options with easier interfaces for accessing census data

Data USA¶

Census Reporter:¶

NHGIS¶

Example: poverty data¶

Via the API:¶

https://api.census.gov/data/2019/acs/acs1/?get=NAME,B17001_002E&for=state:*

Returns JSON¶

Screen%20Shot%202020-09-26%20at%2010.28.06%20PM.png

How to find the right variable names?¶

The census provides web-based documentation:

Overview of all data tables: https://api.census.gov/data.html
All data tables for 2019: https://api.census.gov/data/2019.html
All variables for 2019 ACS 5-year data set: https://api.census.gov/data/2019/acs/acs5/variables.html

A detailed Census API guide¶

https://www.census.gov/data/developers/guidance/api-user-guide.html

Accessing the API is easier from Python¶

Several packages provide easier Python interfaces to census data based on the census API.

We'll focus on cenpy - "Explore and download data from Census APIs"

Example: the racial "dot" map¶

Source

Let's make this for Philadelphia in Python!

# First step: import cenpy
import cenpy

The "explorer" module¶

Functions to help you explore the Census API from Python

cenpy.explorer.available: Returns information about available datasets in Census API
cenpy.explorer.explain: Explain a specific Census dataset
cenpy.explorer.fips_table: Get a table of FIPS codes for a specific geography

Step 1: Identify what dataset we want¶

Today, we'll use the 5-year American Community Survey (latest available year: 2019)
Other common datasets:
- 1-year ACS datasets as well (latest available year: 2019)
- 10-year decennial survey (latest available year: 2010)

available = cenpy.explorer.available()

available.head()

We can use the pandas filter() to search for specific identifiers in the dataframe.

In this case, let's search for the American Community Survey datasets. We'll match index labels using regular expressions.

In particular, we'll search for labels that start with "ACS". In the language of regular expressions, we'll use the "^" to mean "match labels that start with"

For more info on regular expressions, the documentation for the re module is a good place to start.

# Return a dataframe of all datasets that start with "ACS"
# Axis=0 means to filter the index labels!
available.filter(regex="^ACS", axis=0)

Many flavors of ACS datasets are available — we want to use the detailed tables version, specifically the 5-year survey.

The relevant identifiers start with: "ACSDT5Y".

# Return a dataframe of all datasets that start with "ACSDT5Y"
available.filter(regex="^ACSDT5Y", axis=0)

Let's use the latest available data (2019). We can use the explain() function to print out a description of the dataset:

cenpy.explorer.explain("ACSDT5Y2019")

{'American Community Survey: 5-Year Estimates: Detailed Tables 5-Year': 'The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Summary files include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts (116th Congress), all counties, all places, and all tracts and block groups. Summary files contain the most detailed cross-tabulations, many of which are published down to block groups. The data are population and housing counts. There are over 64,000 variables in this dataset.'}

Step 2: Initialize the API connection¶

Use the cenpy.remote.APIConnection object, and pass it the name of the dataset.

acs = cenpy.remote.APIConnection("ACSDT5Y2019")

Step 3: Find the variables we want to load¶

The .variables attribute stores the available variables (across all Census tables).

We can use the varslike() function to search the variables dataframe (it's just a simple wrapper around the pandas filter() function).

len(acs.variables)

27080

acs.variables.head(n=10)

We're interested in variables about hispanic origin broken down by race — let's see if we can find the variables where the "Concept" column starts with "RACE"

acs.varslike?

acs.varslike("HISPANIC OR LATINO ORIGIN BY RACE", by='concept').sort_index() # searches along concept column

It looks like the table we want is "B03002" — we can also easily filter for all variables in this table

variables = [
    "NAME",
    "B03002_001E", # Total
    "B03002_003E", # Not Hispanic, White
    "B03002_004E", # Not Hispanic, Black
    "B03002_005E", # Not Hispanic, American Indian
    "B03002_006E", # Not Hispanic, Asian
    "B03002_007E", # Not Hispanic, Native Hawaiian
    "B03002_008E", # Not Hispanic, Other
    "B03002_009E", # Not Hispanic, Two or More Races
    "B03002_012E", # Hispanic
]

Note: we've also include the "NAME" variable which returns the name of the Census geography we are querying for

Step 4: Identify the geographies to use¶

The Census API use heirarchy of geographies when requesting data.

For example, you cannot just request data for a specific county — you need to specify the state and the county.

Common hierarchies¶

State --> county
State --> place (e.g., cities)
State --> county --> tract
State --> county --> tract --> block group

Tip: Use the `.geographies` attribute¶

This allows you to see:

What geographies are available for a specific dataset
The other required geographies in the heirarchy

acs.geographies['fips']

For the racial dot map, we'll use the most granular available geography: block group.

The hierarchy is: state --> county --> tract --> block group but we can use the * operator for tracts so we'll need to know the FIPS codes for PA and Philadelphia County

counties = cenpy.explorer.fips_table("COUNTY")
counties.head()

# Trim to just Philadelphia
# Search for rows where name contains "Philadelphia"
counties.loc[ counties[3].str.contains("Philadelphia") ]

For Philadelphia County, the FIPS codes are:

Philadelphia County: "101"
PA: "42"

philly_county_code = "101"
pa_state_code = "42"

You can also look up FIPS codes on Google! Wikipedia is usually a trustworthy source...

Step 5: Build the query (finally)¶

We'll use the .query() function, which takes the following arguments:

cols - the list of variables desired from the dataset
geo_unit - string denoting the smallest geographic unit; syntax is "name:FIPS"
geo_filter - dictionary containing groupings of geo_units, if required by the hierarchy

philly_demo_data = acs.query(
    cols=variables,
    geo_unit="block group:*",
    geo_filter={"state": pa_state_code, 
                "county": philly_county_code, 
                "tract": "*"},
)


philly_demo_data.head()

Important: data is returned as strings rather than numeric values¶

for variable in variables:
    
    # Convert all variables EXCEPT for NAME
    if variable != "NAME":
        philly_demo_data[variable] = philly_demo_data[variable].astype(float)

What if we mess up the geographic hierarchy?¶

If you forget to include required parts of the geography heirarchy, you'll get an error!

acs.query(
    cols=variables,
    geo_unit="block group:*",
    geo_filter={"state": pa_state_code},
)

---------------------------------------------------------------------------
JSONDecodeError                           Traceback (most recent call last)
~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/cenpy/remote.py in query(self, cols, geo_unit, geo_filter, apikey, **kwargs)
    218         try:
--> 219             json_content = res.json()
    220             df = pd.DataFrame().from_records(json_content[1:], columns=json_content[0])

~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/requests/models.py in json(self, **kwargs)
    909                     pass
--> 910         return complexjson.loads(self.text, **kwargs)
    911 

~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
    524             and not use_decimal and not kw):
--> 525         return _default_decoder.decode(s)
    526     if cls is None:

~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
    369             s = str(s, self.encoding)
--> 370         obj, end = self.raw_decode(s)
    371         end = _w(s, end).end()

~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
    399                 idx += 3
--> 400         return self.scan_once(s, idx=_w(s, idx).end())

JSONDecodeError: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

HTTPError                                 Traceback (most recent call last)
/var/folders/49/ntrr94q12xd4rq8hqdnx96gm0000gn/T/ipykernel_89330/378178940.py in <module>
----> 1 acs.query(
      2     cols=variables,
      3     geo_unit="block group:*",
      4     geo_filter={"state": pa_state_code},
      5 )

~/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/cenpy/remote.py in query(self, cols, geo_unit, geo_filter, apikey, **kwargs)
    227         except (ValueError, JSONDecodeError):
    228             if res.status_code == 400:
--> 229                 raise r.HTTPError(
    230                     "400 " + "\n".join(map(lambda x: x.decode(), res.iter_lines()))
    231                 )

HTTPError: 400 error: unknown/unsupported geography heirarchy

We need the block group geometries too!¶

cenpy includes an interface to the Census' [Tiger] shapefile database.

cenpy.tiger.available()

[{'name': 'AIANNHA', 'type': 'MapServer'},
 {'name': 'CBSA', 'type': 'MapServer'},
 {'name': 'Hydro', 'type': 'MapServer'},
 {'name': 'Labels', 'type': 'MapServer'},
 {'name': 'Legislative', 'type': 'MapServer'},
 {'name': 'Places_CouSub_ConCity_SubMCD', 'type': 'MapServer'},
 {'name': 'PUMA_TAD_TAZ_UGA_ZCTA', 'type': 'MapServer'},
 {'name': 'Region_Division', 'type': 'MapServer'},
 {'name': 'School', 'type': 'MapServer'},
 {'name': 'Special_Land_Use_Areas', 'type': 'MapServer'},
 {'name': 'State_County', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2012', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2013', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2014', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2015', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2016', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2017', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2018', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2019', 'type': 'MapServer'},
 {'name': 'tigerWMS_ACS2021', 'type': 'MapServer'},
 {'name': 'tigerWMS_Census2010', 'type': 'MapServer'},
 {'name': 'tigerWMS_Census2020', 'type': 'MapServer'},
 {'name': 'tigerWMS_Current', 'type': 'MapServer'},
 {'name': 'tigerWMS_ECON2012', 'type': 'MapServer'},
 {'name': 'tigerWMS_PhysicalFeatures', 'type': 'MapServer'},
 {'name': 'Tracts_Blocks', 'type': 'MapServer'},
 {'name': 'Transportation', 'type': 'MapServer'},
 {'name': 'TribalTracts', 'type': 'MapServer'},
 {'name': 'Urban', 'type': 'MapServer'},
 {'name': 'USLandmass', 'type': 'MapServer'}]

Set the ACS2019 database as the desired GeoService

acs.set_mapservice("tigerWMS_ACS2019")

Connection to American Community Survey: 5-Year Estimates: Detailed Tables 5-Year(ID: https://api.census.gov/data/id/ACSDT5Y2019)
With MapServer: Census ACS 2019 WMS

The map service has many different layers — select the layer for our desired geography

acs.mapservice.layers

[(ESRILayer) 2010 Census Public Use Microdata Areas,
 (ESRILayer) 2010 Census Public Use Microdata Areas Labels,
 (ESRILayer) 2010 Census ZIP Code Tabulation Areas,
 (ESRILayer) 2010 Census ZIP Code Tabulation Areas Labels,
 (ESRILayer) Tribal Census Tracts,
 (ESRILayer) Tribal Census Tracts Labels,
 (ESRILayer) Tribal Block Groups,
 (ESRILayer) Tribal Block Groups Labels,
 (ESRILayer) Census Tracts,
 (ESRILayer) Census Tracts Labels,
 (ESRILayer) Census Block Groups,
 (ESRILayer) Census Block Groups Labels,
 (ESRILayer) Unified School Districts,
 (ESRILayer) Unified School Districts Labels,
 (ESRILayer) Secondary School Districts,
 (ESRILayer) Secondary School Districts Labels,
 (ESRILayer) Elementary School Districts,
 (ESRILayer) Elementary School Districts Labels,
 (ESRILayer) Estates,
 (ESRILayer) Estates Labels,
 (ESRILayer) County Subdivisions,
 (ESRILayer) County Subdivisions Labels,
 (ESRILayer) Subbarrios,
 (ESRILayer) Subbarrios Labels,
 (ESRILayer) Consolidated Cities,
 (ESRILayer) Consolidated Cities Labels,
 (ESRILayer) Incorporated Places,
 (ESRILayer) Incorporated Places Labels,
 (ESRILayer) Census Designated Places,
 (ESRILayer) Census Designated Places Labels,
 (ESRILayer) Alaska Native Regional Corporations,
 (ESRILayer) Alaska Native Regional Corporations Labels,
 (ESRILayer) Tribal Subdivisions,
 (ESRILayer) Tribal Subdivisions Labels,
 (ESRILayer) Federal American Indian Reservations,
 (ESRILayer) Federal American Indian Reservations Labels,
 (ESRILayer) Off-Reservation Trust Lands,
 (ESRILayer) Off-Reservation Trust Lands Labels,
 (ESRILayer) State American Indian Reservations,
 (ESRILayer) State American Indian Reservations Labels,
 (ESRILayer) Hawaiian Home Lands,
 (ESRILayer) Hawaiian Home Lands Labels,
 (ESRILayer) Alaska Native Village Statistical Areas,
 (ESRILayer) Alaska Native Village Statistical Areas Labels,
 (ESRILayer) Oklahoma Tribal Statistical Areas,
 (ESRILayer) Oklahoma Tribal Statistical Areas Labels,
 (ESRILayer) State Designated Tribal Statistical Areas,
 (ESRILayer) State Designated Tribal Statistical Areas Labels,
 (ESRILayer) Tribal Designated Statistical Areas,
 (ESRILayer) Tribal Designated Statistical Areas Labels,
 (ESRILayer) American Indian Joint-Use Areas,
 (ESRILayer) American Indian Joint-Use Areas Labels,
 (ESRILayer) 116th Congressional Districts,
 (ESRILayer) 116th Congressional Districts Labels,
 (ESRILayer) 2018 State Legislative Districts - Upper,
 (ESRILayer) 2018 State Legislative Districts - Upper Labels,
 (ESRILayer) 2018 State Legislative Districts - Lower,
 (ESRILayer) 2018 State Legislative Districts - Lower Labels,
 (ESRILayer) Census Divisions,
 (ESRILayer) Census Divisions Labels,
 (ESRILayer) Census Regions,
 (ESRILayer) Census Regions Labels,
 (ESRILayer) 2010 Census Urbanized Areas,
 (ESRILayer) 2010 Census Urbanized Areas Labels,
 (ESRILayer) 2010 Census Urban Clusters,
 (ESRILayer) 2010 Census Urban Clusters Labels,
 (ESRILayer) Combined New England City and Town Areas,
 (ESRILayer) Combined New England City and Town Areas Labels,
 (ESRILayer) New England City and Town Area Divisions,
 (ESRILayer) New England City and Town Area  Divisions Labels,
 (ESRILayer) Metropolitan New England City and Town Areas,
 (ESRILayer) Metropolitan New England City and Town Areas Labels,
 (ESRILayer) Micropolitan New England City and Town Areas,
 (ESRILayer) Micropolitan New England City and Town Areas Labels,
 (ESRILayer) Combined Statistical Areas,
 (ESRILayer) Combined Statistical Areas Labels,
 (ESRILayer) Metropolitan Divisions,
 (ESRILayer) Metropolitan Divisions Labels,
 (ESRILayer) Metropolitan Statistical Areas,
 (ESRILayer) Metropolitan Statistical Areas Labels,
 (ESRILayer) Micropolitan Statistical Areas,
 (ESRILayer) Micropolitan Statistical Areas Labels,
 (ESRILayer) States,
 (ESRILayer) States Labels,
 (ESRILayer) Counties,
 (ESRILayer) Counties Labels]

acs.mapservice.layers[10]

(ESRILayer) Census Block Groups

Query the service using the .query() function.

We can use a SQL command to request census tracts only for Philadelphia county by specifying the other geographies in the hierachy (in this case, state and county)

acs.mapservice.layers[10].variables

# Use SQL to return geometries only for Philadelphia County in PA
where_clause = f"STATE = {pa_state_code} AND COUNTY = {philly_county_code}"

# Query for block groups
philly_block_groups = acs.mapservice.layers[10].query(where=where_clause)

/Users/nhand/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/pyproj/crs/crs.py:68: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  return _prepare_from_string(" ".join(pjargs))

philly_block_groups.head()

Merge the demographic data with geometries¶

Merge based on multiple columns: state, county, tract, and block group IDs.

The relevant columns are:

"STATE", "COUNTY", "TRACT", "BLKGRP" in the spatial data
"state", "county", "tract", "block group" in the non-spatial data

philly_demo_final = philly_block_groups.merge(
    philly_demo_data,
    left_on=["STATE", "COUNTY", "TRACT", "BLKGRP"],
    right_on=["state", "county", "tract", "block group"],
)

philly_demo_final.head()

Plot it to make sure it makes sense!¶

Using geopandas...

# Check the CRS
philly_demo_final.crs

<Projected CRS: EPSG:3857>
Name: WGS 84 / Pseudo-Mercator
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: World between 85.06°S and 85.06°N.
- bounds: (-180.0, -85.06, 180.0, 85.06)
Coordinate Operation:
- name: Popular Visualisation Pseudo-Mercator
- method: Popular Visualisation Pseudo Mercator
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

fig, ax = plt.subplots(figsize=(10,10))

# Plot the choropleth
philly_demo_final.plot(ax=ax, column='B03002_001E', legend=True)

# Format
ax.set_title("Population of Philadelphia by Block Group", fontsize=16)
ax.set_axis_off()

Or using `hvplot`...¶

cols = ['NAME_x', 'B03002_001E', 'geometry']
philly_demo_final[cols].hvplot(c='B03002_001E',
                              geo=True, 
                              crs=3857, 
                              legend=True,
                              width=600, 
                              height=400, 
                              cmap='viridis')

Let's prep the data for the dot map¶

Rename columns to more user-friendly versions
Add a general "Other" category

# Rename columns
philly_demo_final = philly_demo_final.rename(
    columns={
        "B03002_001E": "Total",  # Total
        "B03002_003E": "White",  # Not Hispanic, White
        "B03002_004E": "Black",  # Not Hispanic, Black
        "B03002_005E": "AI/AN",  # Not Hispanic, American Indian
        "B03002_006E": "Asian",  # Not Hispanic, Asian
        "B03002_007E": "NH/PI",  # Not Hispanic, Native Hawaiian
        "B03002_008E": "Other_",  # Not Hispanic, Other
        "B03002_009E": "Two Plus",  # Not Hispanic, Two or More Races
        "B03002_012E": "Hispanic",  # Hispanic
    }
)

# Add an "Other" column 
cols = ['AI/AN', 'NH/PI','Other_', 'Two Plus']
philly_demo_final['Other'] = philly_demo_final[cols].sum(axis=1)

Define a function to create random points¶

Given a polygon, create randomly distributed points that fall within the polygon.

def random_points_in_polygon(number, polygon):
    """
    Generate a random number of points within the 
    specified polygon.
    """
    points = []
    min_x, min_y, max_x, max_y = polygon.bounds
    i= 0
    while i < number:
        point = Point(np.random.uniform(min_x, max_x), np.random.uniform(min_y, max_y))
        if polygon.contains(point):
            points.append(point)
            i += 1
    return points

Random points example¶

# get the first block group polygon in the data set
geo = philly_demo_final.iloc[0].geometry

geo

fig, ax = plt.subplots(figsize=(6, 6))

# Generate some random points
random_points = random_points_in_polygon(100, geo)

# Plot random points
gpd.GeoSeries(random_points).plot(ax=ax, markersize=20, color="red")

# Plot boundary of block group
gpd.GeoSeries([geo]).plot(ax=ax, facecolor="none", edgecolor="black")

ax.set_axis_off()

def generate_dot_map(data, people_per_dot):
    """
    Given a GeoDataFrame with demographic columns, generate a dot 
    map according to the population in each geometry.
    """
    results = []
    for field in ["White", "Hispanic", "Black", "Asian", "Other"]:

        # generate random points
        pts = data.apply(
            lambda row: random_points_in_polygon(
                row[field] / people_per_dot, row["geometry"]
            ),
            axis=1,
        )

        # combine into single GeoSeries
        pts = gpd.GeoSeries(pts.apply(pd.Series).stack(), crs=data["geometry"].crs)
        pts.name = "geometry"

        # make into a GeoDataFrame
        pts = gpd.GeoDataFrame(pts)
        pts["field"] = field

        # save
        results.append(pts)

    return gpd.GeoDataFrame(pd.concat(results), crs=data["geometry"].crs).reset_index(
        drop=True
    )

Calculate the dot map¶

dot_map = generate_dot_map(philly_demo_final, people_per_dot=50)

print("number of points = ", len(dot_map))

number of points =  34190

dot_map.head()

Now let's plot it¶

# setup a custom color map from ColorBrewer
from matplotlib.colors import ListedColormap

cmap = ListedColormap(
    ["#3a833c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#ffff33"]
)

# plot the dot map
dot_map_3857 = dot_map.to_crs(epsg=3857)

fig, ax = plt.subplots(figsize=(10, 10), facecolor="#cfcfcf")

# Plot
dot_map_3857.plot(
    ax=ax,
    column="field",
    categorical=True,
    legend=True,
    alpha=1,
    markersize=0.5,
    cmap=cmap,
)

# format
ax.set_title("Philadelphia, PA", fontsize=16)
ax.text(
    0.5, 0.95, "1 dot = 50 people", fontsize=12, transform=ax.transAxes, ha="center"
)
ax.set_axis_off()

	id	mag	place	time	updated	tz	url	detail	felt	cdi	mmi	alert	status	sig	net	code	ids	sources	types	nst	dmin	rms	gap	magType	type	title	geometry
0	us6000frdq	4.60	100 km W of San Antonio de los Cobres, Argentina	1633296660451	1633297618040	None	https://earthquake.usgs.gov/earthquakes/eventp...	https://earthquake.usgs.gov/earthquakes/feed/v...	NaN	NaN	NaN	None	reviewed	326	us	6000frdq	,us6000frdq,	,us,	,origin,phase-data,	NaN	1.483	0.39	120.0	mb	earthquake	M 4.6 - 100 km W of San Antonio de los Cobres,...	POINT Z (-67.30740 -24.20710 201.38000)
1	us6000frdl	4.90	82 km SSW of Masachapa, Nicaragua	1633295400195	1633296824040	None	https://earthquake.usgs.gov/earthquakes/eventp...	https://earthquake.usgs.gov/earthquakes/feed/v...	NaN	NaN	NaN	None	reviewed	369	us	6000frdl	,us6000frdl,	,us,	,origin,phase-data,	NaN	1.179	0.77	165.0	mb	earthquake	M 4.9 - 82 km SSW of Masachapa, Nicaragua	POINT Z (-86.82300 11.10590 35.00000)
2	hv72739052	2.52	8 km E of Pāhala, Hawaii	1633293923060	1633294254030	None	https://earthquake.usgs.gov/earthquakes/eventp...	https://earthquake.usgs.gov/earthquakes/feed/v...	NaN	NaN	NaN	None	automatic	98	hv	72739052	,us6000frdb,hv72739052,	,us,hv,	,origin,phase-data,	47.0	NaN	0.13	157.0	ml	earthquake	M 2.5 - 8 km E of Pāhala, Hawaii	POINT Z (-155.39517 19.20717 32.04000)
3	us6000frcz	2.70	51 km S of Whites City, New Mexico	1633291802136	1633292293040	None	https://earthquake.usgs.gov/earthquakes/eventp...	https://earthquake.usgs.gov/earthquakes/feed/v...	NaN	NaN	NaN	None	reviewed	112	us	6000frcz	,us6000frcz,	,us,	,origin,phase-data,	NaN	0.188	0.40	70.0	ml	earthquake	M 2.7 - 51 km S of Whites City, New Mexico	POINT Z (-104.28750 31.71900 12.27000)
4	us6000frcv	4.40	180 km SE of Gorontalo, Indonesia	1633290274834	1633291712040	None	https://earthquake.usgs.gov/earthquakes/eventp...	https://earthquake.usgs.gov/earthquakes/feed/v...	NaN	NaN	NaN	None	reviewed	298	us	6000frcv	,us6000frcv,	,us,	,origin,phase-data,	NaN	1.734	1.17	56.0	mb	earthquake	M 4.4 - 180 km SE of Gorontalo, Indonesia	POINT Z (124.38590 -0.40250 80.46000)

	geometry	OBJECTID	CODE	COD	Shape__Area	Shape__Length
0	POLYGON ((-75.11107 40.04682, -75.11206 40.047...	1	19120	20	9.177970e+07	49921.544063
1	POLYGON ((-75.19227 39.99463, -75.19240 39.994...	2	19121	21	6.959879e+07	39534.887217
2	POLYGON ((-75.15406 39.98601, -75.15494 39.986...	3	19122	22	3.591632e+07	24124.645221
3	POLYGON ((-75.15190 39.97056, -75.15258 39.970...	4	19123	23	3.585175e+07	26421.728982
4	POLYGON ((-75.09660 40.04249, -75.09661 40.042...	5	19124	24	1.448080e+08	63658.770420

	geometry	cartodb_id	objectid	year	dc_key	code	date_	time	race	sex	age	wound	officer_involved	offender_injured	offender_deceased	location	point_x	point_y	dist	inside	outside	fatal
0	POINT (-75.17783 40.07430)	6	1299127	2021	202114012822.0	111	2021-03-11T00:00:00Z	01:20:00	B	M	21	Chest	N	N	N	1200 BLOCK EASTON RD	-75.177833	40.074299	14	0	1	1
1	POINT (-75.16673 40.04550)	7	1299128	2021	202114010813.0	3010	2021-02-27T00:00:00Z	21:24:00	B	M	39	Head	N	N	N	700 BLOCK E PRICE ST	-75.166732	40.045497	14	1	0	1
2	POINT (-75.17915 40.04708)	12	1299133	2021	202114031987.0	111	2021-06-21T00:00:00Z	23:13:00	B	M	26	Chest	N	N	N	6300 BLOCK MORTON ST	-75.179147	40.047077	14	0	1	1
3	POINT (-75.14691 40.06435)	13	1299134	2021	202114018721.0	111	2021-04-10T00:00:00Z	19:39:00	B	M	34	Multiple/Head	N	N	N	1800 BLOCK PLYMOUTH ST	-75.146906	40.064350	14	0	1	1
4	POINT (-75.16866 40.03768)	14	1299135	2021	202114021037.0	111	2021-04-24T00:00:00Z	17:22:00	B	M	26	Back	N	N	N	5500 BLOCK MORTON ST	-75.168663	40.037679	14	0	1	1

	c_isTimeseries	c_isMicrodata	c_isCube	publisher	temporal	spatial	programCode	modified	keyword	contactPoint	distribution	description	bureauCode	accessLevel	title	c_isAvailable	c_isAggregate	c_valuesLink	c_groupsLink	c_examplesLink	c_tagsLink	c_variablesLink	c_geographyLink	c_dataset	vintage
ABSCB2017	NaN	NaN	NaN	U.S. Census Bureau	unidentified	NaN	006:007	2020-04-30 00:00:00.0	()	{'fn': 'ASE Staff', 'hasEmail': 'mailto:erd.an...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Annual Business Survey (ABS) provides info...	006:07	public	Economic Surveys: Annual Business Survey: Annu...	True	True	https://api.census.gov/data/2017/abscb/values....	https://api.census.gov/data/2017/abscb/groups....	https://api.census.gov/data/2017/abscb/example...	https://api.census.gov/data/2017/abscb/tags.json	https://api.census.gov/data/2017/abscb/variabl...	https://api.census.gov/data/2017/abscb/geograp...	(abscb,)	2017.0
ABSCB2018	NaN	NaN	NaN	U.S. Census Bureau	unidentified	NaN	006:007	2020-10-26 00:00:00.0	()	{'fn': 'ASE Staff', 'hasEmail': 'mailto:Erd.an...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Annual Business Survey (ABS) provides info...	006:07	public	Economic Surveys: Annual Business Survey: Annu...	True	True	https://api.census.gov/data/2018/abscb/values....	https://api.census.gov/data/2018/abscb/groups....	https://api.census.gov/data/2018/abscb/example...	https://api.census.gov/data/2018/abscb/tags.json	https://api.census.gov/data/2018/abscb/variabl...	https://api.census.gov/data/2018/abscb/geograp...	(abscb,)	2018.0
ABSCBO2017	NaN	NaN	NaN	U.S. Census Bureau	unidentified	NaN	006:007	2020-04-30 00:00:00.0	()	{'fn': 'ASE Staff', 'hasEmail': 'mailto:erd.an...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Annual Business Survey (ABS) provides info...	006:07	public	Economic Surveys: Annual Business Survey: Annu...	True	True	https://api.census.gov/data/2017/abscbo/values...	https://api.census.gov/data/2017/abscbo/groups...	https://api.census.gov/data/2017/abscbo/exampl...	https://api.census.gov/data/2017/abscbo/tags.json	https://api.census.gov/data/2017/abscbo/variab...	https://api.census.gov/data/2017/abscbo/geogra...	(abscbo,)	2017.0
ABSCBO2018	NaN	NaN	NaN	U.S. Census Bureau	unidentified	NaN	006:007	2020-10-26 00:00:00.0	()	{'fn': 'ASE Staff', 'hasEmail': 'mailto:erd.an...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Annual Business Survey (ABS) provides info...	006:07	public	Economic Surveys: Annual Business Survey: Annu...	True	True	https://api.census.gov/data/2018/abscbo/values...	https://api.census.gov/data/2018/abscbo/groups...	https://api.census.gov/data/2018/abscbo/exampl...	https://api.census.gov/data/2018/abscbo/tags.json	https://api.census.gov/data/2018/abscbo/variab...	https://api.census.gov/data/2018/abscbo/geogra...	(abscbo,)	2018.0
ABSCS2017	NaN	NaN	NaN	U.S. Census Bureau	unidentified	NaN	006:007	2020-04-30 00:00:00.0	()	{'fn': 'ASE Staff', 'hasEmail': 'mailto:erd.an...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Annual Business Survey (ABS) provides info...	006:07	public	Annual Business Survey: Company Summary: 2017	True	True	https://api.census.gov/data/2017/abscs/values....	https://api.census.gov/data/2017/abscs/groups....	https://api.census.gov/data/2017/abscs/example...	https://api.census.gov/data/2017/abscs/tags.json	https://api.census.gov/data/2017/abscs/variabl...	https://api.census.gov/data/2017/abscs/geograp...	(abscs,)	2017.0

	c_isTimeseries	c_isMicrodata	c_isCube	publisher	temporal	spatial	programCode	modified	keyword	contactPoint	distribution	description	bureauCode	accessLevel	title	c_isAvailable	c_isAggregate	c_valuesLink	c_groupsLink	c_examplesLink	c_tagsLink	c_variablesLink	c_geographyLink	c_dataset	vintage
ACSDT5Y2009	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2019-08-27 13:11:18.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	NaN	NaN	American Community Survey: 5-Year Estimates: D...	True	True	https://api.census.gov/data/2009/acs/acs5/valu...	https://api.census.gov/data/2009/acs/acs5/grou...	https://api.census.gov/data/2009/acs/acs5/exam...	https://api.census.gov/data/2009/acs/acs5/tags...	https://api.census.gov/data/2009/acs/acs5/vari...	https://api.census.gov/data/2009/acs/acs5/geog...	(acs, acs5)	2009.0
ACSDT5Y2010	NaN	NaN	True	U.S. Census Bureau	unidentified	United States	006:004	2018-07-04 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	NaN	NaN	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2010/acs/acs5/valu...	https://api.census.gov/data/2010/acs/acs5/grou...	https://api.census.gov/data/2010/acs/acs5/exam...	https://api.census.gov/data/2010/acs/acs5/tags...	https://api.census.gov/data/2010/acs/acs5/vari...	https://api.census.gov/data/2010/acs/acs5/geog...	(acs, acs5)	2010.0
ACSDT5Y2011	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-04 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2011/acs/acs5/valu...	https://api.census.gov/data/2011/acs/acs5/grou...	https://api.census.gov/data/2011/acs/acs5/exam...	https://api.census.gov/data/2011/acs/acs5/tags...	https://api.census.gov/data/2011/acs/acs5/vari...	https://api.census.gov/data/2011/acs/acs5/geog...	(acs, acs5)	2011.0
ACSDT5Y2012	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-04 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2012/acs/acs5/valu...	https://api.census.gov/data/2012/acs/acs5/grou...	https://api.census.gov/data/2012/acs/acs5/exam...	https://api.census.gov/data/2012/acs/acs5/tags...	https://api.census.gov/data/2012/acs/acs5/vari...	https://api.census.gov/data/2012/acs/acs5/geog...	(acs, acs5)	2012.0
ACSDT5Y2013	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-04 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2013/acs/acs5/valu...	https://api.census.gov/data/2013/acs/acs5/grou...	https://api.census.gov/data/2013/acs/acs5/exam...	https://api.census.gov/data/2013/acs/acs5/tags...	https://api.census.gov/data/2013/acs/acs5/vari...	https://api.census.gov/data/2013/acs/acs5/geog...	(acs, acs5)	2013.0
ACSDT5Y2014	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-04 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2014/acs/acs5/valu...	https://api.census.gov/data/2014/acs/acs5/grou...	https://api.census.gov/data/2014/acs/acs5/exam...	https://api.census.gov/data/2014/acs/acs5/tags...	https://api.census.gov/data/2014/acs/acs5/vari...	https://api.census.gov/data/2014/acs/acs5/geog...	(acs, acs5)	2014.0
ACSDT5Y2015	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-05 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2015/acs/acs5/valu...	https://api.census.gov/data/2015/acs/acs5/grou...	https://api.census.gov/data/2015/acs/acs5/exam...	https://api.census.gov/data/2015/acs/acs5/tags...	https://api.census.gov/data/2015/acs/acs5/vari...	https://api.census.gov/data/2015/acs/acs5/geog...	(acs, acs5)	2015.0
ACSDT5Y2016	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-07-05 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2016/acs/acs5/valu...	https://api.census.gov/data/2016/acs/acs5/grou...	https://api.census.gov/data/2016/acs/acs5/exam...	https://api.census.gov/data/2016/acs/acs5/tags...	https://api.census.gov/data/2016/acs/acs5/vari...	https://api.census.gov/data/2016/acs/acs5/geog...	(acs, acs5)	2016.0
ACSDT5Y2017	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2018-08-21 07:11:43.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	ACS 5-Year Detailed Tables	True	True	https://api.census.gov/data/2017/acs/acs5/valu...	https://api.census.gov/data/2017/acs/acs5/grou...	https://api.census.gov/data/2017/acs/acs5/exam...	https://api.census.gov/data/2017/acs/acs5/tags...	https://api.census.gov/data/2017/acs/acs5/vari...	https://api.census.gov/data/2017/acs/acs5/geog...	(acs, acs5)	2017.0
ACSDT5Y2018	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2019-10-22 16:28:02.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	American Community Survey: 5-Year Estimates: D...	True	True	https://api.census.gov/data/2018/acs/acs5/valu...	https://api.census.gov/data/2018/acs/acs5/grou...	https://api.census.gov/data/2018/acs/acs5/exam...	https://api.census.gov/data/2018/acs/acs5/tags...	https://api.census.gov/data/2018/acs/acs5/vari...	https://api.census.gov/data/2018/acs/acs5/geog...	(acs, acs5)	2018.0
ACSDT5Y2019	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2020-04-03 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Community Survey (ACS) is an ongo...	006:07	public	American Community Survey: 5-Year Estimates: D...	True	True	https://api.census.gov/data/2019/acs/acs5/valu...	https://api.census.gov/data/2019/acs/acs5/grou...	https://api.census.gov/data/2019/acs/acs5/exam...	https://api.census.gov/data/2019/acs/acs5/tags...	https://api.census.gov/data/2019/acs/acs5/vari...	https://api.census.gov/data/2019/acs/acs5/geog...	(acs, acs5)	2019.0
ACSDT5YAIAN2010	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2019-10-24 07:18:57.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Indian and Alaska Native (AIAN) t...	NaN	NaN	American Community Survey: 5-Year Estimates: A...	True	True	https://api.census.gov/data/2010/acs/acs5/aian...	https://api.census.gov/data/2010/acs/acs5/aian...	https://api.census.gov/data/2010/acs/acs5/aian...	https://api.census.gov/data/2010/acs/acs5/aian...	https://api.census.gov/data/2010/acs/acs5/aian...	https://api.census.gov/data/2010/acs/acs5/aian...	(acs, acs5, aian)	2010.0
ACSDT5YAIAN2015	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2020-02-13 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The American Indian and Alaska Native (AIAN) t...	NaN	NaN	ACS 5-Year AIAN Detailed Tables	True	True	https://api.census.gov/data/2015/acs/acs5/aian...	https://api.census.gov/data/2015/acs/acs5/aian...	https://api.census.gov/data/2015/acs/acs5/aian...	https://api.census.gov/data/2015/acs/acs5/aian...	https://api.census.gov/data/2015/acs/acs5/aian...	https://api.census.gov/data/2015/acs/acs5/aian...	(acs, acs5, aian)	2015.0
ACSDT5YSPT2010	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2019-10-11 14:16:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Selected Population Tables (SPT) are rele...	NaN	NaN	American Community Survey: 5-Year Estimates: S...	True	True	https://api.census.gov/data/2010/acs/acs5/spt/...	https://api.census.gov/data/2010/acs/acs5/spt/...	https://api.census.gov/data/2010/acs/acs5/spt/...	https://api.census.gov/data/2010/acs/acs5/spt/...	https://api.census.gov/data/2010/acs/acs5/spt/...	https://api.census.gov/data/2010/acs/acs5/spt/...	(acs, acs5, spt)	2010.0
ACSDT5YSPT2015	NaN	NaN	True	U.S. Census Bureau	unidentified	NaN	006:004	2020-02-18 00:00:00.0	()	{'fn': 'American Community Survey Office', 'ha...	{'@type': 'dcat:Distribution', 'accessURL': 'h...	The Selected Population Tables (SPT) are rele...	NaN	NaN	American Community Survey: 5-Year Estimates: S...	True	True	https://api.census.gov/data/2015/acs/acs5/spt/...	https://api.census.gov/data/2015/acs/acs5/spt/...	https://api.census.gov/data/2015/acs/acs5/spt/...	https://api.census.gov/data/2015/acs/acs5/spt/...	https://api.census.gov/data/2015/acs/acs5/spt/...	https://api.census.gov/data/2015/acs/acs5/spt/...	(acs, acs5, spt)	2015.0

	geometry	objectid	year	dc_key	code	date_	time	race	sex	age	wound	officer_involved	offender_injured	offender_deceased	location	point_x	point_y	dist	outside
0	POINT (-75.16119 40.04582)	1299122	2021	202114009208.0	411	2021-02-20T00:00:00Z	19:40:00	B	M	44	Multiple	N	N	N	5600 BLOCK BOYER ST	-75.161190	40.045822	14	1.0
1	POINT (-75.17558 40.07988)	1299123	2021	202114009327.0	411	2021-02-21T00:00:00Z	15:25:00	B	M	21	Multiple/Head	N	N	N	1500 BLOCK GOWEN AVE	-75.175579	40.079880	14	1.0
2	POINT (-75.16983 40.06031)	1299124	2021	202114010227.0	0411	2021-02-25T00:00:00Z	19:18:00	B	M	15	Hand	N	N	N	1300 BLOCK E CLIVEDEN ST	-75.169833	40.060313	14	1.0
3	POINT (-75.16392 40.04296)	1299125	2021	202114033835.0	0411	2021-07-01T00:00:00Z	12:25:00	B	M	29	Back	N	N	N	700 BLOCK E LOCUST ave	-75.163923	40.042964	14	1.0
4	POINT (-75.16392 40.04296)	1299126	2021	202114033835.0	0411	2021-07-01T00:00:00Z	16:25:00	B	M	20	Buttocks	N	N	N	700 BLOCK E LOCUST AVE	-75.163923	40.042964	14	1.0

	geometry	objectid	year	dc_key	code	date_	time	race	sex	age	wound	officer_involved	offender_injured	offender_deceased	location	point_x	point_y	dist	outside	OBJECTID	Shape__Area	Shape__Length
0	POINT (-8366905.398 4872603.244)	1299122	2021	202114009208.0	411	2021-02-20T00:00:00Z	19:40:00	B	M	44	Multiple	N	N	N	5600 BLOCK BOYER ST	-75.161190	40.045822	14	1.0	1	3.970706e+09	394751.12047
1	POINT (-8368507.174 4877557.022)	1299123	2021	202114009327.0	411	2021-02-21T00:00:00Z	15:25:00	B	M	21	Multiple/Head	N	N	N	1500 BLOCK GOWEN AVE	-75.175579	40.079880	14	1.0	1	3.970706e+09	394751.12047
2	POINT (-8367867.533 4874710.675)	1299124	2021	202114010227.0	0411	2021-02-25T00:00:00Z	19:18:00	B	M	15	Hand	N	N	N	1300 BLOCK E CLIVEDEN ST	-75.169833	40.060313	14	1.0	1	3.970706e+09	394751.12047
3	POINT (-8367209.634 4872187.657)	1299125	2021	202114033835.0	0411	2021-07-01T00:00:00Z	12:25:00	B	M	29	Back	N	N	N	700 BLOCK E LOCUST ave	-75.163923	40.042964	14	1.0	1	3.970706e+09	394751.12047
4	POINT (-8367209.634 4872187.657)	1299126	2021	202114033835.0	0411	2021-07-01T00:00:00Z	16:25:00	B	M	20	Buttocks	N	N	N	700 BLOCK E LOCUST AVE	-75.163923	40.042964	14	1.0	1	3.970706e+09	394751.12047

	label	concept	predicateType	group	predicateOnly	hasGeoCollectionSupport	attributes	required
for	Census API FIPS 'for' clause	Census API Geography Specification	fips-for	N/A	True	NaN	NaN	NaN
in	Census API FIPS 'in' clause	Census API Geography Specification	fips-in	N/A	True	NaN	NaN	NaN
ucgid	Uniform Census Geography Identifier clause	Census API Geography Specification	ucgid	N/A	True	True	NaN	NaN
B24022_060E	Estimate!!Total:!!Female:!!Service occupations...	SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE P...	int	B24022	NaN	NaN	B24022_060EA,B24022_060M,B24022_060MA	NaN
B19001B_014E	Estimate!!Total:!!$100,000 to $124,999	HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...	int	B19001B	NaN	NaN	B19001B_014EA,B19001B_014M,B19001B_014MA	NaN
B07007PR_019E	Estimate!!Total:!!Moved from different municip...	GEOGRAPHICAL MOBILITY IN THE PAST YEAR BY CITI...	int	B07007PR	NaN	NaN	B07007PR_019EA,B07007PR_019M,B07007PR_019MA	NaN
B19101A_004E	Estimate!!Total:!!$15,000 to $19,999	FAMILY INCOME IN THE PAST 12 MONTHS (IN 2019 I...	int	B19101A	NaN	NaN	B19101A_004EA,B19101A_004M,B19101A_004MA	NaN
B24022_061E	Estimate!!Total:!!Female:!!Service occupations...	SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE P...	int	B24022	NaN	NaN	B24022_061EA,B24022_061M,B24022_061MA	NaN
B19001B_013E	Estimate!!Total:!!$75,000 to $99,999	HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...	int	B19001B	NaN	NaN	B19001B_013EA,B19001B_013M,B19001B_013MA	NaN
B07007PR_018E	Estimate!!Total:!!Moved from different municip...	GEOGRAPHICAL MOBILITY IN THE PAST YEAR BY CITI...	int	B07007PR	NaN	NaN	B07007PR_018EA,B07007PR_018M,B07007PR_018MA	NaN

	label	concept	predicateType	group	predicateOnly	hasGeoCollectionSupport	attributes	required
B03002_001E	Estimate!!Total:	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_001EA,B03002_001M,B03002_001MA	NaN
B03002_002E	Estimate!!Total:!!Not Hispanic or Latino:	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_002EA,B03002_002M,B03002_002MA	NaN
B03002_003E	Estimate!!Total:!!Not Hispanic or Latino:!!Whi...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_003EA,B03002_003M,B03002_003MA	NaN
B03002_004E	Estimate!!Total:!!Not Hispanic or Latino:!!Bla...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_004EA,B03002_004M,B03002_004MA	NaN
B03002_005E	Estimate!!Total:!!Not Hispanic or Latino:!!Ame...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_005EA,B03002_005M,B03002_005MA	NaN
B03002_006E	Estimate!!Total:!!Not Hispanic or Latino:!!Asi...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_006EA,B03002_006M,B03002_006MA	NaN
B03002_007E	Estimate!!Total:!!Not Hispanic or Latino:!!Nat...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_007EA,B03002_007M,B03002_007MA	NaN
B03002_008E	Estimate!!Total:!!Not Hispanic or Latino:!!Som...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_008EA,B03002_008M,B03002_008MA	NaN
B03002_009E	Estimate!!Total:!!Not Hispanic or Latino:!!Two...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_009EA,B03002_009M,B03002_009MA	NaN
B03002_010E	Estimate!!Total:!!Not Hispanic or Latino:!!Two...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_010EA,B03002_010M,B03002_010MA	NaN
B03002_011E	Estimate!!Total:!!Not Hispanic or Latino:!!Two...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_011EA,B03002_011M,B03002_011MA	NaN
B03002_012E	Estimate!!Total:!!Hispanic or Latino:	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_012EA,B03002_012M,B03002_012MA	NaN
B03002_013E	Estimate!!Total:!!Hispanic or Latino:!!White a...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_013EA,B03002_013M,B03002_013MA	NaN
B03002_014E	Estimate!!Total:!!Hispanic or Latino:!!Black o...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_014EA,B03002_014M,B03002_014MA	NaN
B03002_015E	Estimate!!Total:!!Hispanic or Latino:!!America...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_015EA,B03002_015M,B03002_015MA	NaN
B03002_016E	Estimate!!Total:!!Hispanic or Latino:!!Asian a...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_016EA,B03002_016M,B03002_016MA	NaN
B03002_017E	Estimate!!Total:!!Hispanic or Latino:!!Native ...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_017EA,B03002_017M,B03002_017MA	NaN
B03002_018E	Estimate!!Total:!!Hispanic or Latino:!!Some ot...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_018EA,B03002_018M,B03002_018MA	NaN
B03002_019E	Estimate!!Total:!!Hispanic or Latino:!!Two or ...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_019EA,B03002_019M,B03002_019MA	NaN
B03002_020E	Estimate!!Total:!!Hispanic or Latino:!!Two or ...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_020EA,B03002_020M,B03002_020MA	NaN
B03002_021E	Estimate!!Total:!!Hispanic or Latino:!!Two or ...	HISPANIC OR LATINO ORIGIN BY RACE	int	B03002	NaN	NaN	B03002_021EA,B03002_021M,B03002_021MA	NaN
GEO_ID	Geography	FAMILY INCOME IN THE PAST 12 MONTHS (IN 2019 I...	string	B17015,B18104,B17016,B18105,B17017,B18106,B170...	NaN	NaN	NAME	NaN

	name	geoLevelDisplay	referenceDate	requires	wildcard	optionalWithWCFor
0	us	010	2019-01-01	NaN	NaN	NaN
1	region	020	2019-01-01	NaN	NaN	NaN
2	division	030	2019-01-01	NaN	NaN	NaN
3	state	040	2019-01-01	NaN	NaN	NaN
4	county	050	2019-01-01	[state]	[state]	state
5	county subdivision	060	2019-01-01	[state, county]	[county]	county
6	subminor civil division	067	2019-01-01	[state, county, county subdivision]	NaN	NaN
7	place/remainder (or part)	070	2019-01-01	[state, county, county subdivision]	NaN	NaN
8	tract	140	2019-01-01	[state, county]	[county]	county
9	block group	150	2019-01-01	[state, county, tract]	[county, tract]	tract
10	county (or part)	155	2019-01-01	[state, place]	NaN	NaN
11	place	160	2019-01-01	[state]	[state]	state
12	consolidated city	170	2019-01-01	[state]	[state]	state
13	place (or part)	172	2019-01-01	[state, consolidated city]	NaN	NaN
14	alaska native regional corporation	230	2019-01-01	[state]	[state]	state
15	american indian area/alaska native area/hawaii...	250	2019-01-01	NaN	NaN	NaN
16	american indian tribal subdivision	251	2019-01-01	[american indian area/alaska native area/hawai...	[american indian area/alaska native area/hawai...	american indian area/alaska native area/hawaii...
17	american indian area/alaska native area (reser...	252	2019-01-01	NaN	NaN	NaN
18	american indian area (off-reservation trust la...	254	2019-01-01	NaN	NaN	NaN
19	tribal census tract	256	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
20	tribal block group	258	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
21	state (or part)	260	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
22	place (or part)	269	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
23	county (or part)	270	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
24	american indian area/alaska native area/hawaii...	280	2019-01-01	[state]	NaN	NaN
25	american indian area/alaska native area (reser...	283	2019-01-01	[state]	NaN	NaN
26	american indian area (off-reservation trust la...	286	2019-01-01	[state]	NaN	NaN
27	state (or part)	290	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
28	tribal census tract (or part)	291	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
29	tribal census tract (or part)	292	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
30	tribal block group (or part)	293	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
31	tribal block group (or part)	294	2019-01-01	[american indian area/alaska native area/hawai...	NaN	NaN
32	metropolitan statistical area/micropolitan sta...	310	2019-01-01	NaN	NaN	NaN
33	state (or part)	311	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
34	principal city (or part)	312	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
35	county	313	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
36	metropolitan division	314	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
37	state (or part)	315	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
38	county	316	2019-01-01	[metropolitan statistical area/micropolitan st...	NaN	NaN
39	metropolitan statistical area/micropolitan sta...	320	2019-01-01	[state]	NaN	NaN
40	principal city (or part)	321	2019-01-01	[state, metropolitan statistical area/micropol...	NaN	NaN
41	county	322	2019-01-01	[state, metropolitan statistical area/micropol...	NaN	NaN
42	metropolitan division (or part)	323	2019-01-01	[state, metropolitan statistical area/micropol...	NaN	NaN
43	county	324	2019-01-01	[state, metropolitan statistical area/micropol...	NaN	NaN
44	combined statistical area	330	2019-01-01	NaN	NaN	NaN
45	state (or part)	331	2019-01-01	[combined statistical area]	NaN	NaN
46	metropolitan statistical area/micropolitan sta...	332	2019-01-01	[combined statistical area]	NaN	NaN
47	state (or part)	333	2019-01-01	[combined statistical area, metropolitan stati...	NaN	NaN
48	combined new england city and town area	335	2019-01-01	NaN	NaN	NaN
49	state (or part)	336	2019-01-01	[combined new england city and town area]	NaN	NaN
50	new england city and town area	337	2019-01-01	[combined new england city and town area]	NaN	NaN
51	state (or part)	338	2019-01-01	[combined new england city and town area, new ...	NaN	NaN
52	combined statistical area (or part)	340	2019-01-01	[state]	NaN	NaN
53	metropolitan statistical area/micropolitan sta...	341	2019-01-01	[state, combined statistical area (or part)]	NaN	NaN
54	combined new england city and town area (or part)	345	2019-01-01	[state]	NaN	NaN
55	new england city and town area (or part)	346	2019-01-01	[state, combined new england city and town are...	NaN	NaN
56	new england city and town area	350	2019-01-01	NaN	NaN	NaN
57	state (or part)	351	2019-01-01	[new england city and town area]	NaN	NaN
58	principal city	352	2019-01-01	[new england city and town area, state (or part)]	[state (or part)]	state (or part)
59	county (or part)	353	2019-01-01	[new england city and town area, state (or part)]	[state (or part)]	state (or part)
60	county subdivision	354	2019-01-01	[new england city and town area, state (or par...	[county (or part)]	county (or part)
61	necta division	355	2019-01-01	[new england city and town area]	NaN	NaN
62	state (or part)	356	2019-01-01	[new england city and town area, necta division]	NaN	NaN
63	county (or part)	357	2019-01-01	[new england city and town area, necta divisio...	NaN	NaN
64	county subdivision	358	2019-01-01	[new england city and town area, necta divisio...	NaN	NaN
65	new england city and town area (or part)	360	2019-01-01	[state]	NaN	NaN
66	principal city	361	2019-01-01	[state, new england city and town area (or part)]	NaN	NaN
67	county (or part)	362	2019-01-01	[state, new england city and town area (or part)]	NaN	NaN
68	county subdivision	363	2019-01-01	[state, new england city and town area (or par...	NaN	NaN
69	necta division (or part)	364	2019-01-01	[state, new england city and town area (or part)]	NaN	NaN
70	county (or part)	365	2019-01-01	[state, new england city and town area (or par...	NaN	NaN
71	county subdivision	366	2019-01-01	[state, new england city and town area (or par...	NaN	NaN
72	urban area	400	2019-01-01	NaN	NaN	NaN
73	state (or part)	410	2019-01-01	[urban area]	NaN	NaN
74	county (or part)	430	2019-01-01	[urban area, state (or part)]	NaN	NaN
75	congressional district	500	2019-01-01	[state]	[state]	state
76	county (or part)	510	2019-01-01	[state, congressional district]	NaN	NaN
77	american indian area/alaska native area/hawaii...	550	2019-01-01	[state, congressional district]	NaN	NaN
78	state legislative district (upper chamber)	610	2019-01-01	[state]	NaN	NaN
79	county (or part)	612	2019-01-01	[state, state legislative district (upper cham...	NaN	NaN
80	state legislative district (lower chamber)	620	2019-01-01	[state]	NaN	NaN
81	county (or part)	622	2019-01-01	[state, state legislative district (lower cham...	NaN	NaN
82	public use microdata area	795	2019-01-01	[state]	[state]	state
83	zip code tabulation area	860	2019-01-01	[state]	[state]	state
84	school district (elementary)	950	2019-01-01	[state]	NaN	NaN
85	school district (secondary)	960	2019-01-01	[state]	NaN	NaN
86	school district (unified)	970	2019-01-01	[state]	NaN	NaN

	0	1	2	3	4
0	AL	1	1	Autauga County	H1
1	AL	1	3	Baldwin County	H1
2	AL	1	5	Barbour County	H1
3	AL	1	7	Bibb County	H1
4	AL	1	9	Blount County	H1

	NAME	B03002_001E	B03002_003E	B03002_004E	B03002_006E	B03002_008E	B03002_009E	B03002_012E	state	county	tract	block group
0	Block Group 1, Census Tract 9807, Philadelphia...	0	0	0	0	0	0	0	42	101	980700	1
1	Block Group 3, Census Tract 27.01, Philadelphi...	1955	904	262	502	0	36	251	42	101	002701	3
2	Block Group 2, Census Tract 337.01, Philadelph...	976	654	0	99	41	103	79	42	101	033701	2
3	Block Group 3, Census Tract 337.01, Philadelph...	3859	1594	477	305	0	182	1301	42	101	033701	3
4	Block Group 2, Census Tract 205, Philadelphia ...	1017	36	796	124	0	0	61	42	101	020500	2

	name	type	alias	length	domain
0	MTFCC	esriFieldTypeString	MTFCC	5.0	None
1	OID	esriFieldTypeString	OID	22.0	None
2	GEOID	esriFieldTypeString	GEOID	12.0	None
3	STATE	esriFieldTypeString	STATE	2.0	None
4	COUNTY	esriFieldTypeString	COUNTY	3.0	None
5	TRACT	esriFieldTypeString	TRACT	6.0	None
6	BLKGRP	esriFieldTypeString	BLKGRP	1.0	None
7	BASENAME	esriFieldTypeString	BASENAME	100.0	None
8	NAME	esriFieldTypeString	NAME	100.0	None
9	LSADC	esriFieldTypeString	LSADC	2.0	None
10	FUNCSTAT	esriFieldTypeString	FUNCSTAT	1.0	None
11	AREALAND	esriFieldTypeDouble	AREALAND	NaN	None
12	AREAWATER	esriFieldTypeDouble	AREAWATER	NaN	None
13	STGEOMETRY	esriFieldTypeGeometry	STGEOMETRY	NaN	None
14	CENTLAT	esriFieldTypeString	CENTLAT	11.0	None
15	CENTLON	esriFieldTypeString	CENTLON	12.0	None
16	INTPTLAT	esriFieldTypeString	INTPTLAT	11.0	None
17	INTPTLON	esriFieldTypeString	INTPTLON	12.0	None
18	OBJECTID	esriFieldTypeOID	OBJECTID	NaN	None

	MTFCC	OID	GEOID	STATE	COUNTY	TRACT	BLKGRP	BASENAME	NAME	LSADC	FUNCSTAT	AREALAND	CENTLAT	CENTLON	INTPTLAT	INTPTLON	OBJECTID	geometry
0	G5030	20859508776554	421010090004	42	101	009000	4	4	Block Group 4	BG	S	127839	+39.9572985	-075.1914596	+39.9572985	-075.1914596	3312	POLYGON ((-8370567.253 4859618.633, -8370565.4...
1	G5030	20859508883786	421010188002	42	101	018800	2	2	Block Group 2	BG	S	59722	+39.9975515	-075.1023594	+39.9975515	-075.1023594	3294	POLYGON ((-8360576.329 4865634.793, -8360515.3...
2	G5030	20859508883559	421010382002	42	101	038200	2	2	Block Group 2	BG	S	250653	+39.9901972	-075.1026825	+39.9901972	-075.1026825	3295	POLYGON ((-8360910.732 4864437.488, -8360638.5...
3	G5030	20859508934956	421010090001	42	101	009000	1	1	Block Group 1	BG	S	67291	+39.9616601	-075.1923616	+39.9616601	-075.1923616	3741	POLYGON ((-8370579.498 4860512.096, -8370429.3...
4	G5030	20859508776526	421010090002	42	101	009000	2	2	Block Group 2	BG	S	83926	+39.9597607	-075.1914912	+39.9597607	-075.1914912	3742	POLYGON ((-8370547.215 4860205.940, -8370524.1...

	geometry	field
0	POINT (-8370182.039 4859825.380)	White
1	POINT (-8370078.324 4859636.855)	White
2	POINT (-8370456.109 4859665.820)	White
3	POINT (-8370466.705 4859831.167)	White
4	POINT (-8370137.971 4859655.019)	White

Week 5AGetting Data Part 1: Working with APIs